Alvaro Barbeira

14.8k total citations · 1 hit paper
14 papers, 1.3k citations indexed

About

Alvaro Barbeira is a scholar working on Molecular Biology, Genetics and Cancer Research. According to data from OpenAlex, Alvaro Barbeira has authored 14 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 9 papers in Genetics and 3 papers in Cancer Research. Recurrent topics in Alvaro Barbeira's work include Genetic Associations and Epidemiology (9 papers), RNA modifications and cancer (5 papers) and Bioinformatics and Genomic Networks (4 papers). Alvaro Barbeira is often cited by papers focused on Genetic Associations and Epidemiology (9 papers), RNA modifications and cancer (5 papers) and Bioinformatics and Genomic Networks (4 papers). Alvaro Barbeira collaborates with scholars based in United States, United Kingdom and Sweden. Alvaro Barbeira's co-authors include Hae Kyung Im, David A. Knowles, Jonathan K. Pritchard, Scott Dickinson, Jack Humphrey, Yang Li, Milton Pividori, Arno Ruusalepp, Johan Björkegren and Ke Hao and has published in prestigious journals such as Nature Communications, Nature Genetics and The American Journal of Human Genetics.

In The Last Decade

Alvaro Barbeira

13 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Opportunities and challenges for transcriptome-wide association studies

2019 506 citations Michael Wainberg, Nasa Sinnott-Armstrong et al. Nature Genetics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Alvaro Barbeira United States	12	887	682	171	81	62	14	1.3k
Sung Chun United States	11	771 0.9×	813 1.2×	206 1.2×	78 1.0×	44 0.7×	15	1.4k
Michele Pinelli Italy	19	745 0.8×	457 0.7×	170 1.0×	55 0.7×	144 2.3×	49	1.3k
Arthur Ko United States	14	613 0.7×	534 0.8×	270 1.6×	69 0.9×	72 1.2×	22	1.1k
Kaanan P. Shah United States	4	706 0.8×	702 1.0×	102 0.6×	58 0.7×	69 1.1×	5	1.1k
Sahar V. Mozaffari United States	7	689 0.8×	691 1.0×	113 0.7×	51 0.6×	65 1.0×	7	1.1k
Armin Schoech United States	9	583 0.7×	910 1.3×	84 0.5×	79 1.0×	87 1.4×	9	1.3k
Eija H. Seppälä Finland	17	436 0.5×	417 0.6×	89 0.5×	88 1.1×	94 1.5×	34	1.1k
Alison R. Barton United States	10	494 0.6×	368 0.5×	206 1.2×	34 0.4×	68 1.1×	11	840
Luke J. O’Connor United States	15	638 0.7×	923 1.4×	91 0.5×	97 1.2×	137 2.2×	23	1.4k
Patrice Roll France	19	615 0.7×	222 0.3×	129 0.8×	53 0.7×	52 0.8×	54	1.0k

Countries citing papers authored by Alvaro Barbeira

Since Specialization

Citations

This map shows the geographic impact of Alvaro Barbeira's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Alvaro Barbeira with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alvaro Barbeira more than expected).

Fields of papers citing papers by Alvaro Barbeira

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alvaro Barbeira. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Alvaro Barbeira. The network helps show where Alvaro Barbeira may publish in the future.

Co-authorship network of co-authors of Alvaro Barbeira

This figure shows the co-authorship network connecting the top 25 collaborators of Alvaro Barbeira. A scholar is included among the top collaborators of Alvaro Barbeira based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Alvaro Barbeira. Alvaro Barbeira is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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14 of 14 papers shown

Barbeira, Alvaro, et al.. (2024). A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer. The American Journal of Human Genetics. 111(6). 1100–1113. 4 indexed citations

Gao, Guimin, et al.. (2023). A joint transcriptome-wide association study across multiple tissues identifies candidate breast cancer susceptibility genes. The American Journal of Human Genetics. 110(6). 950–962. 12 indexed citations

Liang, Yanyu, François Aguet, Alvaro Barbeira, Kristin Ardlie, & Hae Kyung Im. (2021). A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction. Nature Communications. 12(1). 1424–1424. 14 indexed citations

Barbeira, Alvaro, Yanyu Liang, Rodrigo Bonazzola, et al.. (2020). Fine‐mapping and QTL tissue‐sharing information improves the reliability of causal gene identification. Genetic Epidemiology. 44(8). 854–867. 26 indexed citations

He, Yuan, Surya B. Chhetri, Marios Arvanitis, et al.. (2020). sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression. Genome biology. 21(1). 235–235. 11 indexed citations

Zhang, Yuhua, Corbin Quick, Ketian Yu, et al.. (2020). PTWAS: investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis. Genome biology. 21(1). 232–232. 45 indexed citations

Pividori, Milton, Padma Sheila Rajagopal, Alvaro Barbeira, et al.. (2020). PhenomeXcan: Mapping the genome to the phenome through the transcriptome. Science Advances. 6(37). 60 indexed citations

Gay, Nicole R., Michael J. Gloudemans, Margaret L. Antonio, et al.. (2020). Impact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx. Genome biology. 21(1). 233–233. 55 indexed citations

Barbeira, Alvaro, et al.. (2020). hakyimlab/gtex-gwas-analysis: zenodo-release.v1.0. Zenodo (CERN European Organization for Nuclear Research).

10.

Wainberg, Michael, Nasa Sinnott-Armstrong, Nicholas Mancuso, et al.. (2019). Opportunities and challenges for transcriptome-wide association studies. Nature Genetics. 51(4). 592–599. 506 indexed citations breakdown →

11.

Barbeira, Alvaro, Milton Pividori, Jiamao Zheng, et al.. (2019). Integrating predicted transcriptome from multiple tissues improves association detection. PLoS Genetics. 15(1). e1007889–e1007889. 169 indexed citations

12.

Wheeler, Heather E., Alvaro Barbeira, Rodrigo Bonazzola, et al.. (2019). Imputed gene associations identify replicable trans ‐acting genes enriched in transcription pathways and complex traits. Genetic Epidemiology. 43(6). 596–608. 14 indexed citations

13.

Geeleher, Paul, Aritro Nath, Fan Wang, et al.. (2018). Cancer expression quantitative trait loci (eQTLs) can be determined from heterogeneous tumor gene expression data by modeling variation in tumor purity. Genome biology. 19(1). 130–130. 22 indexed citations

14.

Li, Yang, David A. Knowles, Jack Humphrey, et al.. (2017). Annotation-free quantification of RNA splicing using LeafCutter. Nature Genetics. 50(1). 151–158. 358 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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